Abstract: Social networks are complex systems composed of interdependent organizations and people with diverse network structures. Understanding network dynamics, such as exchange commitment, requires a methodological toolkit that does not assume away complexity. In this study, we extend a technique for analyzing longitudinal, multilayer network data called network alignment. We introduce a novel metric – intersect proportions – for analyzing similarity between divergent graphs. We demonstrate the application of network alignment and intersect proportions to the context of investor commitment to startups and entrepreneurs. Using this technique, we are able to disentangle exchange commitment across complex networks.